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TIL
  • MAIN
  • : TIL?
  • : WIL
  • : Plan
  • : Retrospective
    • 21Y
      • Wait a moment!
      • 9M 2W
      • 9M1W
      • 8M4W
      • 8M3W
      • 8M2W
      • 8M1W
      • 7M4W
      • 7M3W
      • 7M2W
      • 7M1W
      • 6M5W
      • 1H
    • ์ƒˆ์‚ฌ๋žŒ ๋˜๊ธฐ ํ”„๋กœ์ ํŠธ
      • 2ํšŒ์ฐจ
      • 1ํšŒ์ฐจ
  • TIL : ML
    • Paper Analysis
      • BERT
      • Transformer
    • Boostcamp 2st
      • [S]Data Viz
        • (4-3) Seaborn ์‹ฌํ™”
        • (4-2) Seaborn ๊ธฐ์ดˆ
        • (4-1) Seaborn ์†Œ๊ฐœ
        • (3-4) More Tips
        • (3-3) Facet ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-2) Color ์‚ฌ์šฉํ•˜๊ธฐ
        • (3-1) Text ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-3) Scatter Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-2) Line Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (2-1) Bar Plot ์‚ฌ์šฉํ•˜๊ธฐ
        • (1-3) Python๊ณผ Matplotlib
        • (1-2) ์‹œ๊ฐํ™”์˜ ์š”์†Œ
        • (1-1) Welcome to Visualization (OT)
      • [P]MRC
        • (2๊ฐ•) Extraction-based MRC
        • (1๊ฐ•) MRC Intro & Python Basics
      • [P]KLUE
        • (5๊ฐ•) BERT ๊ธฐ๋ฐ˜ ๋‹จ์ผ ๋ฌธ์žฅ ๋ถ„๋ฅ˜ ๋ชจ๋ธ ํ•™์Šต
        • (4๊ฐ•) ํ•œ๊ตญ์–ด BERT ์–ธ์–ด ๋ชจ๋ธ ํ•™์Šต
        • [NLP] ๋ฌธ์žฅ ๋‚ด ๊ฐœ์ฒด๊ฐ„ ๊ด€๊ณ„ ์ถ”์ถœ
        • (3๊ฐ•) BERT ์–ธ์–ด๋ชจ๋ธ ์†Œ๊ฐœ
        • (2๊ฐ•) ์ž์—ฐ์–ด์˜ ์ „์ฒ˜๋ฆฌ
        • (1๊ฐ•) ์ธ๊ณต์ง€๋Šฅ๊ณผ ์ž์—ฐ์–ด ์ฒ˜๋ฆฌ
      • [U]Stage-CV
      • [U]Stage-NLP
        • 7W Retrospective
        • (10๊ฐ•) Advanced Self-supervised Pre-training Models
        • (09๊ฐ•) Self-supervised Pre-training Models
        • (08๊ฐ•) Transformer (2)
        • (07๊ฐ•) Transformer (1)
        • 6W Retrospective
        • (06๊ฐ•) Beam Search and BLEU score
        • (05๊ฐ•) Sequence to Sequence with Attention
        • (04๊ฐ•) LSTM and GRU
        • (03๊ฐ•) Recurrent Neural Network and Language Modeling
        • (02๊ฐ•) Word Embedding
        • (01๊ฐ•) Intro to NLP, Bag-of-Words
        • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Preprocessing for NMT Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Subword-level Language Model
        • [ํ•„์ˆ˜ ๊ณผ์ œ2] RNN-based Language Model
        • [์„ ํƒ ๊ณผ์ œ] BERT Fine-tuning with transformers
        • [ํ•„์ˆ˜ ๊ณผ์ œ] Data Preprocessing
      • Mask Wear Image Classification
        • 5W Retrospective
        • Report_Level1_6
        • Performance | Review
        • DAY 11 : HardVoting | MultiLabelClassification
        • DAY 10 : Cutmix
        • DAY 9 : Loss Function
        • DAY 8 : Baseline
        • DAY 7 : Class Imbalance | Stratification
        • DAY 6 : Error Fix
        • DAY 5 : Facenet | Save
        • DAY 4 : VIT | F1_Loss | LrScheduler
        • DAY 3 : DataSet/Lodaer | EfficientNet
        • DAY 2 : Labeling
        • DAY 1 : EDA
        • 2_EDA Analysis
      • [P]Stage-1
        • 4W Retrospective
        • (10๊ฐ•) Experiment Toolkits & Tips
        • (9๊ฐ•) Ensemble
        • (8๊ฐ•) Training & Inference 2
        • (7๊ฐ•) Training & Inference 1
        • (6๊ฐ•) Model 2
        • (5๊ฐ•) Model 1
        • (4๊ฐ•) Data Generation
        • (3๊ฐ•) Dataset
        • (2๊ฐ•) Image Classification & EDA
        • (1๊ฐ•) Competition with AI Stages!
      • [U]Stage-3
        • 3W Retrospective
        • PyTorch
          • (10๊ฐ•) PyTorch Troubleshooting
          • (09๊ฐ•) Hyperparameter Tuning
          • (08๊ฐ•) Multi-GPU ํ•™์Šต
          • (07๊ฐ•) Monitoring tools for PyTorch
          • (06๊ฐ•) ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
          • (05๊ฐ•) Dataset & Dataloader
          • (04๊ฐ•) AutoGrad & Optimizer
          • (03๊ฐ•) PyTorch ํ”„๋กœ์ ํŠธ ๊ตฌ์กฐ ์ดํ•ดํ•˜๊ธฐ
          • (02๊ฐ•) PyTorch Basics
          • (01๊ฐ•) Introduction to PyTorch
      • [U]Stage-2
        • 2W Retrospective
        • DL Basic
          • (10๊ฐ•) Generative Models 2
          • (09๊ฐ•) Generative Models 1
          • (08๊ฐ•) Sequential Models - Transformer
          • (07๊ฐ•) Sequential Models - RNN
          • (06๊ฐ•) Computer Vision Applications
          • (05๊ฐ•) Modern CNN - 1x1 convolution์˜ ์ค‘์š”์„ฑ
          • (04๊ฐ•) Convolution์€ ๋ฌด์—‡์ธ๊ฐ€?
          • (03๊ฐ•) Optimization
          • (02๊ฐ•) ๋‰ด๋Ÿด ๋„คํŠธ์›Œํฌ - MLP (Multi-Layer Perceptron)
          • (01๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ณธ ์šฉ์–ด ์„ค๋ช… - Historical Review
        • Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Multi-headed Attention Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] LSTM Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] CNN Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] Optimization Assignment
          • [ํ•„์ˆ˜ ๊ณผ์ œ] MLP Assignment
      • [U]Stage-1
        • 1W Retrospective
        • AI Math
          • (AI Math 10๊ฐ•) RNN ์ฒซ๊ฑธ์Œ
          • (AI Math 9๊ฐ•) CNN ์ฒซ๊ฑธ์Œ
          • (AI Math 8๊ฐ•) ๋ฒ ์ด์ฆˆ ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 7๊ฐ•) ํ†ต๊ณ„ํ•™ ๋ง›๋ณด๊ธฐ
          • (AI Math 6๊ฐ•) ํ™•๋ฅ ๋ก  ๋ง›๋ณด๊ธฐ
          • (AI Math 5๊ฐ•) ๋”ฅ๋Ÿฌ๋‹ ํ•™์Šต๋ฐฉ๋ฒ• ์ดํ•ดํ•˜๊ธฐ
          • (AI Math 4๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ๋งค์šด๋ง›
          • (AI Math 3๊ฐ•) ๊ฒฝ์‚ฌํ•˜๊ฐ•๋ฒ• - ์ˆœํ•œ๋ง›
          • (AI Math 2๊ฐ•) ํ–‰๋ ฌ์ด ๋ญ์˜ˆ์š”?
          • (AI Math 1๊ฐ•) ๋ฒกํ„ฐ๊ฐ€ ๋ญ์˜ˆ์š”?
        • Python
          • (Python 7-2๊ฐ•) pandas II
          • (Python 7-1๊ฐ•) pandas I
          • (Python 6๊ฐ•) numpy
          • (Python 5-2๊ฐ•) Python data handling
          • (Python 5-1๊ฐ•) File / Exception / Log Handling
          • (Python 4-2๊ฐ•) Module and Project
          • (Python 4-1๊ฐ•) Python Object Oriented Programming
          • (Python 3-2๊ฐ•) Pythonic code
          • (Python 3-1๊ฐ•) Python Data Structure
          • (Python 2-4๊ฐ•) String and advanced function concept
          • (Python 2-3๊ฐ•) Conditionals and Loops
          • (Python 2-2๊ฐ•) Function and Console I/O
          • (Python 2-1๊ฐ•) Variables
          • (Python 1-3๊ฐ•) ํŒŒ์ด์ฌ ์ฝ”๋”ฉ ํ™˜๊ฒฝ
          • (Python 1-2๊ฐ•) ํŒŒ์ด์ฌ ๊ฐœ์š”
          • (Python 1-1๊ฐ•) Basic computer class for newbies
        • Assignment
          • [์„ ํƒ ๊ณผ์ œ 3] Maximum Likelihood Estimate
          • [์„ ํƒ ๊ณผ์ œ 2] Backpropagation
          • [์„ ํƒ ๊ณผ์ œ 1] Gradient Descent
          • [ํ•„์ˆ˜ ๊ณผ์ œ 5] Morsecode
          • [ํ•„์ˆ˜ ๊ณผ์ œ 4] Baseball
          • [ํ•„์ˆ˜ ๊ณผ์ œ 3] Text Processing 2
          • [ํ•„์ˆ˜ ๊ณผ์ œ 2] Text Processing 1
          • [ํ•„์ˆ˜ ๊ณผ์ œ 1] Basic Math
    • ๋”ฅ๋Ÿฌ๋‹ CNN ์™„๋ฒฝ ๊ฐ€์ด๋“œ - Fundamental ํŽธ
      • ์ข…ํ•ฉ ์‹ค์Šต 2 - ์บ๊ธ€ Plant Pathology(๋‚˜๋ฌด์žŽ ๋ณ‘ ์ง„๋‹จ) ๊ฒฝ์—ฐ ๋Œ€ํšŒ
      • ์ข…ํ•ฉ ์‹ค์Šต 1 - 120์ข…์˜ Dog Breed Identification ๋ชจ๋ธ ์ตœ์ ํ™”
      • ์‚ฌ์ „ ํ›ˆ๋ จ ๋ชจ๋ธ์˜ ๋ฏธ์„ธ ์กฐ์ • ํ•™์Šต๊ณผ ๋‹ค์–‘ํ•œ Learning Rate Scheduler์˜ ์ ์šฉ
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - ResNet ์ƒ์„ธ์™€ EfficientNet ๊ฐœ์š”
      • Advanced CNN ๋ชจ๋ธ ํŒŒํ—ค์น˜๊ธฐ - AlexNet, VGGNet, GoogLeNet
      • Albumentation์„ ์ด์šฉํ•œ Augmentation๊ธฐ๋ฒ•๊ณผ Keras Sequence ํ™œ์šฉํ•˜๊ธฐ
      • ์‚ฌ์ „ ํ›ˆ๋ จ CNN ๋ชจ๋ธ์˜ ํ™œ์šฉ๊ณผ Keras Generator ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์ดํ•ด
      • ๋ฐ์ดํ„ฐ ์ฆ๊ฐ•์˜ ์ดํ•ด - Keras ImageDataGenerator ํ™œ์šฉ
      • CNN ๋ชจ๋ธ ๊ตฌํ˜„ ๋ฐ ์„ฑ๋Šฅ ํ–ฅ์ƒ ๊ธฐ๋ณธ ๊ธฐ๋ฒ• ์ ์šฉํ•˜๊ธฐ
    • AI School 1st
    • ํ˜„์—… ์‹ค๋ฌด์ž์—๊ฒŒ ๋ฐฐ์šฐ๋Š” Kaggle ๋จธ์‹ ๋Ÿฌ๋‹ ์ž…๋ฌธ
    • ํŒŒ์ด์ฌ ๋”ฅ๋Ÿฌ๋‹ ํŒŒ์ดํ† ์น˜
  • TIL : Python & Math
    • Do It! ์žฅ๊ณ +๋ถ€ํŠธ์ŠคํŠธ๋žฉ: ํŒŒ์ด์ฌ ์›น๊ฐœ๋ฐœ์˜ ์ •์„
      • Relations - ๋‹ค๋Œ€๋‹ค ๊ด€๊ณ„
      • Relations - ๋‹ค๋Œ€์ผ ๊ด€๊ณ„
      • ํ…œํ”Œ๋ฆฟ ํŒŒ์ผ ๋ชจ๋“ˆํ™” ํ•˜๊ธฐ
      • TDD (Test Driven Development)
      • template tags & ์กฐ๊ฑด๋ฌธ
      • ์ •์  ํŒŒ์ผ(static files) & ๋ฏธ๋””์–ด ํŒŒ์ผ(media files)
      • FBV (Function Based View)์™€ CBV (Class Based View)
      • Django ์ž…๋ฌธํ•˜๊ธฐ
      • ๋ถ€ํŠธ์ŠคํŠธ๋žฉ
      • ํ”„๋ก ํŠธ์—”๋“œ ๊ธฐ์ดˆ๋‹ค์ง€๊ธฐ (HTML, CSS, JS)
      • ๋“ค์–ด๊ฐ€๊ธฐ + ํ™˜๊ฒฝ์„ค์ •
    • Algorithm
      • Programmers
        • Level1
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์ˆซ์ž ๋ฌธ์ž์—ด๊ณผ ์˜๋‹จ์–ด
          • ์ž์—ฐ์ˆ˜ ๋’ค์ง‘์–ด ๋ฐฐ์—ด๋กœ ๋งŒ๋“ค๊ธฐ
          • ์ •์ˆ˜ ๋‚ด๋ฆผ์ฐจ์ˆœ์œผ๋กœ ๋ฐฐ์น˜ํ•˜๊ธฐ
          • ์ •์ˆ˜ ์ œ๊ณฑ๊ทผ ํŒ๋ณ„
          • ์ œ์ผ ์ž‘์€ ์ˆ˜ ์ œ๊ฑฐํ•˜๊ธฐ
          • ์ง์‚ฌ๊ฐํ˜• ๋ณ„์ฐ๊ธฐ
          • ์ง์ˆ˜์™€ ํ™€์ˆ˜
          • ์ฒด์œก๋ณต
          • ์ตœ๋Œ€๊ณต์•ฝ์ˆ˜์™€ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • ์ฝœ๋ผ์ธ  ์ถ”์ธก
          • ํฌ๋ ˆ์ธ ์ธํ˜•๋ฝ‘๊ธฐ ๊ฒŒ์ž„
          • ํ‚คํŒจ๋“œ ๋ˆ„๋ฅด๊ธฐ
          • ํ‰๊ท  ๊ตฌํ•˜๊ธฐ
          • ํฐ์ผ“๋ชฌ
          • ํ•˜์ƒค๋“œ ์ˆ˜
          • ํ•ธ๋“œํฐ ๋ฒˆํ˜ธ ๊ฐ€๋ฆฌ๊ธฐ
          • ํ–‰๋ ฌ์˜ ๋ง์…ˆ
        • Level2
          • ์ˆซ์ž์˜ ํ‘œํ˜„
          • ์ˆœ์œ„ ๊ฒ€์ƒ‰
          • ์ˆ˜์‹ ์ตœ๋Œ€ํ™”
          • ์†Œ์ˆ˜ ์ฐพ๊ธฐ
          • ์†Œ์ˆ˜ ๋งŒ๋“ค๊ธฐ
          • ์‚ผ๊ฐ ๋‹ฌํŒฝ์ด
          • ๋ฌธ์ž์—ด ์••์ถ•
          • ๋ฉ”๋‰ด ๋ฆฌ๋‰ด์–ผ
          • ๋” ๋งต๊ฒŒ
          • ๋•…๋”ฐ๋จน๊ธฐ
          • ๋ฉ€์ฉกํ•œ ์‚ฌ๊ฐํ˜•
          • ๊ด„ํ˜ธ ํšŒ์ „ํ•˜๊ธฐ
          • ๊ด„ํ˜ธ ๋ณ€ํ™˜
          • ๊ตฌ๋ช…๋ณดํŠธ
          • ๊ธฐ๋Šฅ ๊ฐœ๋ฐœ
          • ๋‰ด์Šค ํด๋Ÿฌ์Šคํ„ฐ๋ง
          • ๋‹ค๋ฆฌ๋ฅผ ์ง€๋‚˜๋Š” ํŠธ๋Ÿญ
          • ๋‹ค์Œ ํฐ ์ˆซ์ž
          • ๊ฒŒ์ž„ ๋งต ์ตœ๋‹จ๊ฑฐ๋ฆฌ
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
          • ๊ฐ€์žฅ ํฐ ์ •์‚ฌ๊ฐํ˜• ์ฐพ๊ธฐ
          • H-Index
          • JadenCase ๋ฌธ์ž์—ด ๋งŒ๋“ค๊ธฐ
          • N๊ฐœ์˜ ์ตœ์†Œ๊ณต๋ฐฐ์ˆ˜
          • N์ง„์ˆ˜ ๊ฒŒ์ž„
          • ๊ฐ€์žฅ ํฐ ์ˆ˜
          • 124 ๋‚˜๋ผ์˜ ์ˆซ์ž
          • 2๊ฐœ ์ดํ•˜๋กœ ๋‹ค๋ฅธ ๋น„ํŠธ
          • [3์ฐจ] ํŒŒ์ผ๋ช… ์ •๋ ฌ
          • [3์ฐจ] ์••์ถ•
          • ์ค„ ์„œ๋Š” ๋ฐฉ๋ฒ•
          • [3์ฐจ] ๋ฐฉ๊ธˆ ๊ทธ๊ณก
          • ๊ฑฐ๋ฆฌ๋‘๊ธฐ ํ™•์ธํ•˜๊ธฐ
        • Level3
          • ๋งค์นญ ์ ์ˆ˜
          • ์™ธ๋ฒฝ ์ ๊ฒ€
          • ๊ธฐ์ง€๊ตญ ์„ค์น˜
          • ์ˆซ์ž ๊ฒŒ์ž„
          • 110 ์˜ฎ๊ธฐ๊ธฐ
          • ๊ด‘๊ณ  ์ œ๊ฑฐ
          • ๊ธธ ์ฐพ๊ธฐ ๊ฒŒ์ž„
          • ์…”ํ‹€๋ฒ„์Šค
          • ๋‹จ์†์นด๋ฉ”๋ผ
          • ํ‘œ ํŽธ์ง‘
          • N-Queen
          • ์ง•๊ฒ€๋‹ค๋ฆฌ ๊ฑด๋„ˆ๊ธฐ
          • ์ตœ๊ณ ์˜ ์ง‘ํ•ฉ
          • ํ•ฉ์Šน ํƒ์‹œ ์š”๊ธˆ
          • ๊ฑฐ์Šค๋ฆ„๋ˆ
          • ํ•˜๋…ธ์ด์˜ ํƒ‘
          • ๋ฉ€๋ฆฌ ๋›ฐ๊ธฐ
          • ๋ชจ๋‘ 0์œผ๋กœ ๋งŒ๋“ค๊ธฐ
        • Level4
    • Head First Python
    • ๋ฐ์ดํ„ฐ ๋ถ„์„์„ ์œ„ํ•œ SQL
    • ๋‹จ ๋‘ ์žฅ์˜ ๋ฌธ์„œ๋กœ ๋ฐ์ดํ„ฐ ๋ถ„์„๊ณผ ์‹œ๊ฐํ™” ๋ฝ€๊ฐœ๊ธฐ
    • Linear Algebra(Khan Academy)
    • ์ธ๊ณต์ง€๋Šฅ์„ ์œ„ํ•œ ์„ ํ˜•๋Œ€์ˆ˜
    • Statistics110
  • TIL : etc
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Kubernetes
    • [๋”ฐ๋ฐฐ๋Ÿฐ] Docker
      • 2. ๋„์ปค ์„ค์น˜ ์‹ค์Šต 1 - ํ•™์ŠตํŽธ(์ค€๋น„๋ฌผ/์‹ค์Šต ์œ ํ˜• ์†Œ๊ฐœ)
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  • ๊ฐ€์ƒํ™”ํ ํˆฌ์ž๊ณต์‹
  • PART2. ํˆฌ์ž ์‹ฌ๋ฆฌ
  • ํˆฌ์ž ์ตœ๋Œ€์˜ ์ ์€ ์ž์‹ ์˜ ์ƒ๊ฐ
  • ๋‡Œ์˜ ํŽธํ–ฅ์„ ๊ทน๋ณตํ•˜๋Š” ๋ฒ•

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  1. 2021 TIL
  2. APR

29 Thu

๊ฐ€์ƒํ™”ํ ํˆฌ์ž๊ณต์‹

PART2. ํˆฌ์ž ์‹ฌ๋ฆฌ

ํˆฌ์ž ์ตœ๋Œ€์˜ ์ ์€ ์ž์‹ ์˜ ์ƒ๊ฐ

  • ํˆฌ์ž๋ฅผ ๋ชจ๋ฅผ ๋•Œ : ์ธ๊ฐ„

  • ํˆฌ์ž๋ฅผ ํ•˜๊ธฐ ์ „ : ์›์ˆญ์ด

  • ํˆฌ์ž๋ฅผ ํ•œ ํ›„ : ๋ฌผ๊ณ ๊ธฐ

๊ฐ์œผ๋กœ ํˆฌ์žํ•˜๋ฉด ๋งํ•œ๋‹ค. ๊ฐ์œผ๋กœ ์„ฑ๊ณตํ•˜๋Š” ์‚ฌ๋žŒ์€ 0.01% ๋„ ์•ˆ๋œ๋‹ค. ์ด๋Š” ์ธ๊ฐ„์ด ์ถ”๋ก  ์ฒด๊ณ„๋ฅผ ํ†ตํ•ด ํŒ๋‹จํ•˜๋Š” ๊ฒƒ์ด ์•„๋‹Œ ์ง๊ด€ ์ฒด๊ณ„๋กœ ํŒ๋‹จํ•˜๋Š” ์ผ์ด ๋งŽ๊ธฐ ๋•Œ๋ฌธ. ํˆฌ์ž์—๋„ ์ด ์ง๊ด€์ฒด๊ณ„๊ฐ€ ์ ์šฉ๋œ๋‹ค.

๋น„์ผ๊ด€์„ฑ

  • ํˆฌ์ž๋Š” ์ผ๊ด€์„ฑ์ด ์ค‘์š”ํ•˜๋‹ค.

  • ๊ธฐ๋ถ„, ๊ฑด๊ฐ•, ๋ฐฐ๊ณ ํ””, ํ”ผ๋กœ, ๋‚ ์”จ ๋“ฑ์œผ๋กœ ํˆฌ์ž ๊ฒฐ์ •์„ ์ขŒ์šฐํ•  ๊ฐ€๋Šฅ์„ฑ์ด ์žˆ์œผ๋ฉฐ ์ด๋Ÿฌํ•œ ํŒ๋‹จ ์˜ค๋ฅ˜๋Š” ์น˜๋ช…์ ์ธ ํˆฌ์ž ๊ฒฐ๊ณผ๋ฅผ ๋‚ณ๋Š”๋‹ค.

๊ณผ์ž‰ ํ™•์‹  ํŽธํ–ฅ

  • ์ž์‹ ์ด ๋‚จ๋ณด๋‹ค ์ž˜๋‚ฌ๋‹ค๊ณ  ์ƒ๊ฐํ•œ๋‹ค. ๊ฐ€๋ฐฉ๋ˆ์ด ๊ธธ์ˆ˜๋ก ์ด๋Ÿฐ ๊ฒฝํ–ฅ์ด ๊ฐ•ํ•ด์ง„๋‹ค. ์ด๋ฅผ ๊ณผ์ž‰ ํ™•์‹  ํŽธํ–ฅ, overconfidence bias ๋ผ๊ณ  ํ•œ๋‹ค.

    • ์ž์‹ ์˜ ์šด์ „ ์‹ค๋ ฅ์€? ์ด๋ผ๋Š” ์งˆ๋ฌธ์— 80% ์ด์ƒ์ด ํ‰๊ท  ์ด์ƒ์ด๋ผ๊ณ  ๋‹ตํ•˜์ง€๋งŒ, ์ง๊ด€์ ์œผ๋กœ ํ‰๊ท  ์‹ค๋ ฅ ์ด์ƒ์„ ๋ณด์œ ํ•˜๋Š” ์šด์ „์ž๋Š” 50%๊ฐ€ ๋„˜์„ ์ˆ˜ ์—†๋‹ค.

๊ธฐ์ค€์  ํŽธํ–ฅ

  • ๋‹น๋ฉด ๋ฌธ์ œ์™€ ์ „ํ˜€ ์ƒ๊ด€์—†๋Š” ์ˆซ์ž๋‚˜ ํŒฉํŠธ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฒฐ์ •์„ ๋‚ด๋ฆฌ๋Š” ํ˜„์ƒ์„ ๊ธฐ์ค€์  ํŽธํ–ฅ, anchoring bias ๋ผ๊ณ  ํ•œ๋‹ค.

    • ์˜ค๋Š˜ ์•„์นจ 40์ด๋ผ๋Š” ์ˆซ์ž๋ฅผ ๋ณธ ๋‹น์‹ , ํ˜„์žฌ ์‹œ์•„์ฝ”์ธ์ด 40์›์ด๋ผ ์ด๋ฅผ ๊ตฌ๋งคํ•˜๊ฒŒ ๋˜๋Š”๋ฐ...

  • ๊ฐ€์ƒํ™”ํ ์‹œ์žฅ์—์„œ ์ค‘์š”ํ•œ ๊ธฐ์ค€์€ ๋งค์ˆ˜ ๊ฐ€๊ฒฉ์ด๋‹ค.

์†์‹ค ํšŒํ”ผ ํ˜„์ƒ + ์ฒ˜๋ถ„ ํšจ๊ณผ

  • ๋ณธ์ „ ๋ฐ‘์œผ๋กœ๋Š” ์ ˆ๋Œ€ ๋ชปํŒ๋‹ค๊ณ  ํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์€ ์†์‹ค ํšŒํ”ผ ํ˜„์ƒ์— ๋น ์ ธ ์žˆ๋‹ค๊ณ  ๋งํ•  ์ˆ˜ ์žˆ๋‹ค.

    • ์ผ๋ฐ˜์ ์œผ๋กœ 100๋งŒ์›์„ ๋ฒ„๋Š” ๊ธฐ์จ๋ณด๋‹ค ์žƒ๋Š” ์Šฌํ””์ด 3๋ฐฐ๋Š” ๋” ๊ฐ•ํ•˜๋‹ค.

  • ์†์‹ค์„ ๋ณด๋ฉด ๋ณธ์ „์€ ๋งŒํšŒํ•ด์•ผ์ง€๋ฅผ ์™ธ์น˜๋ฉฐ ํŒ”์ง€ ๋ชปํ•˜๊ณ , ๊ฐ€๊ฒฉ์ด ์˜ค๋ฅด๋ฉด ์žฌ๋นจ๋ฆฌ ์ด์ต์„ ์‹คํ˜„ํ•˜๋ ค๊ณ  ํ•œ๋‹ค. ์ด๋ ‡๊ฒŒ ์ด์ต์„ ๋„ˆ๋ฌด ๋นจ๋ฆฌ ์ฑ™๊ธฐ๋Š” ํ–‰์œ„๋ฅผ ์ฒ˜๋ถ„ ํšจ๊ณผ ๋ผ๊ณ  ํ•œ๋‹ค.

  • ์ด์ต์€ ๊ธธ๊ฒŒ, ์†์‹ค์€ ์งง๊ฒŒ ๊ฐ€์ ธ๊ฐ€๋Š” ๊ฒƒ์ด ํŠธ๋ ˆ์ด๋”ฉ์˜ ์ •์„ ๋„˜๋ฒ„์›์ด๋‹ค.

    • ํˆฌ์ž์˜ ์‹คํŒจ์ž๋“ค์€ ์†์‹ค์€ ๊ธธ๊ฒŒ(๋ณธ์ „์„ ๋ฒŒ์–ด์•ผ ๋ผ) ์ด์ต์€ ์งง๊ฒŒ(์–ผ๋ฅธ ๋–จ์–ด์ง€๊ธฐ ์ „์— ํŒ”์ž) ๊ฐ€์ ธ๊ฐ„๋‹ค. ์ด๋Ÿฌ๋ฉด ์†์ต๋น„(์ด์ต๊ณผ ์†์‹ค์˜ ๋น„์œจ)์ด ๋งค์šฐ ๋‚ฎ์•„์ง„๋‹ค.

ํ™•์ฆ ํŽธํ–ฅ

  • ์‚ฌ๋žŒ์€ ์ž์‹ ์ด ๋“ฃ๊ณ  ์‹ถ๊ฑฐ๋‚˜ ๋ฏฟ๊ณ  ์‹ถ์€ ๋ง๋งŒ ๋“ฃ๋Š” ๊ฒฝํ–ฅ์ด ์žˆ๋Š”๋ฐ, ์ด๋ฅผ ํ™•์ฆ ํŽธํ–ฅ ์ด๋ผ๊ณ  ํ•œ๋‹ค.

    • ๋‚ด๊ฐ€ ์‚ฐ ์ฝ”์ธ์— ๋Œ€ํ•ด 10๋ช…์ด ์‚ด๋งŒํ–ˆ๋‹ค๊ณ  ๋งํ•ด์ฃผ๋ฉด ์•ˆ์ •๊ฐ์„ ๋А๋ผ๊ฒŒ ๋  ๊ฒƒ์ด๋‹ค.

  • ์ด๊ธด ๋‚ ์€ ๋‚ด ์ „๋žต์ด ์˜ณ๊ณ  ์ง„ ๋‚ ์€ ์šด์ด ๋‚˜๋นด๋‹ค๊ณ  ์ƒ๊ฐํ•˜๋ฉด ์•ˆ๋œ๋‹ค.

ํ†ต๊ณ„ ๋ฌด์ง€

  • ๋ˆ„๊ตฐ๊ฐ€ "์ตœ๊ทผ์— 5์ผ๊ฐ„ 30% ๋–จ์–ด์ง„ ์ฝ”์ธ์„ ์‚ฌ๊ณ  ์ดํ‹€ ๋’ค์— ํŒ”๋ฉด ์ด๋“์ด๋ž˜" ๋ผ๊ณ  ํ•œ๋‹ค๋ฉด ์ด ๋ง์„ ๋ถ„์„ํ•  ์ค„ ์•Œ์•„์•ผ ํ•œ๋‹ค.

  • ์ตœ๊ทผ 5์ผ๊ฐ„ 30% ํ•˜๋ฝํ•œ ๋‚ ์ด ์žˆ๋Š” ์ฝ”์ธ๋“ค์„ ์ถ”ํ•ฉํ•˜๊ณ , ๊ทธ ๋‚ ๊ณผ ์ดํ‹€ ํ›„ ๊ฐ€๊ฒฉ์„ ๋น„๊ตํ•˜๊ณ , ์‹ค์ œ๋กœ ํˆฌ์žํ–ˆ๋‹ค๋ฉด ์–ผ๋งˆ์˜ ์ˆ˜์ต์„ ๋ƒˆ๊ณ , ์Šน๋ฅ ์€ ์–ด๋– ํ•˜๋ฉฐ, ์†์ต๋น„๋Š” ์–ผ๋งˆ์ผ์ง€ ๋“ฑ์„ ๊ณ„์‚ฐํ•ด์•ผ ํ•œ๋‹ค.

    • ๋ฐ”๋กœ ์ด ์ž‘์—…์„ ๋ฐฑํ…Œ์ŠคํŒ… ์ด๋ผ๊ณ  ํ•œ๋‹ค.

  • ๋˜, ๋Œ€๋ถ€๋ถ„ ํˆฌ์ž์ž๋Š” "5์Šน 0ํŒจ" ๊ฐ™์€ ๋†€๋ผ์šด ์Šน๋ฅ ์— ํ˜นํ•˜๋Š”๋ฐ, 5์ „ ์ด๋ผ๋Š” ์‹œ๋„๋Š” ํ‘œ๋ณธ ์ˆ˜๊ฐ€ ๋„ˆ๋ฌด ์ ๋‹ค. ์ด ๋ง์€ ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜๋ฏธ ํ•˜์ง€ ์•Š๋‹ค๋Š” ๋œป. ์ด๋ ‡๊ฒŒ ์ ์€ ํ‘œ๋ณธ ์ง‘๋‹จ์„ ๊ฐ€์ง€๊ณ  ์˜์‚ฌ๊ฒฐ์ •์„ ํ•˜๋Š” ํŽธํ–ฅ์„ ์†Œ์ˆ˜์˜ ๋ฒ•์น™ ์ด๋ผ๊ณ  ํ•œ๋‹ค.

์Šคํ† ๋ฆฌํ…”๋ง์˜ ํํ•ด

  • ์นด๋”๋ผ์˜ ์œ„๋ ฅ

  • "๋น„ํŠธํ† ๋ ŒํŠธ๊ฐ€ ์ตœ๊ทผ์— ๊ต‰์žฅํžˆ ์ƒ์Šนํ–ˆ์–ด, 20์›๋„ ๋ณผ๋งŒํ•  ๋“ฏ! ๊ฐ€์ฆˆ์•„~"

    • ์•„๋ฌด ๊ทผ๊ฑฐ ์—†๋Š” "์†Œ์Œ"

ํˆฌ์ž ์ค‘๋…

  • ์ˆ˜์ต์„ ํ™•์ •์ง€์„ ๋•Œ์˜ ๋А๋‚Œ์„ ๋‹ค์‹œ ์–ป์œผ๋ ค ํ•จ

  • ์žƒ์œผ๋ฉด ํšŒ๋ณต ์‹ฌ๋ฆฌ ๋ฐœ์ƒ

  • ๊ฒฐ๊ตญ, ๋ณธ์—…์— ์ถฉ์‹คํ•˜์ง€ ๋ชปํ•˜๋Š” ์ผ๊นŒ์ง€ ์•ผ๊ธฐ

๋‡Œ์˜ ํŽธํ–ฅ์„ ๊ทน๋ณตํ•˜๋Š” ๋ฒ•

  • ๋งค๋„ ์‹œ์ ๊นŒ์ง€ ๊ฒฐ์ •ํ•˜๊ณ  ๋งค์ˆ˜ ํ•˜๋ผ

    • ์–ด๋””๊นŒ์ง€ ์˜ค๋ฅด๊ณ , ์–ด๋””๊นŒ์ง€ ๋‚ด๋ฆด์ง€ ์–ด์ผ€ ์•ˆ๋‹ค๊ณ  ๊ทธ๊ฑธ ๊ฒฐ์ •ํ•จ?

    • ์ž์‹ ๋งŒ์˜ ์ˆ˜์ต๋ฅ ์„ ์ •ํ•ด์•ผํ•œ๋‹ค.

      • ๋–จ์–ด์ง€๋ฉด : 20% ๋–จ์–ด์ง€๋ฉด ๋งค๋„

      • ์˜ค๋ฅด๋ฉด : 20% ์˜ค๋ฅด๋ฉด ๋งค๋„

      • ํšก๋ณด : ํ•œ ๋‹ฌ ํ›„ ๋งค๋„

    • ๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋“  ์ƒํ™ฉ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋งค๋„ ์ „๋žต์€ ์กด์žฌํ•˜์ง€ ์•Š๋Š”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์†์ต๋น„๋ฅผ ๋†’์ผ ์ˆ˜ ์žˆ๋Š” ์ „๋žต์€ ๋งŽ์ด ์กด์žฌํ•˜๋Š”๋ฐ ์ด๋Š” ์ดํ›„์— ๋‹ค๋ฃธ

  • ํ•œ๋ฒˆ ์ •ํ•œ ์ „๋žต์€ ์ผ๊ด€์„ฑ์„ ์œ ์ง€ํ•  ๊ฒƒ

    • 2์‹œ๊ฐ„ ์ „์—๋Š” 20% ์˜ฌ๋ž๋Š”๋ฐ, ๋‚ด๊ฐ€ ํŒ”๊ณ ์ž ์ •ํ•œ ์‹œ๊ฐ„์—๋Š” 10% ๋–จ์–ด์ ธ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿด ๊ฒฝ์šฐ ์ „๋žต์„ ์ž„์˜๋กœ ๋ฐ”๊พธ๊ณ ์ž ํ•˜๋Š” ์œ ํ˜น์— ๋น ์ง€๊ฒŒ ๋˜๋Š”๋ฐ ๊ทธ๋Ÿฌ๋ฉด ์•ˆ๋œ๋‹ค!

      • ์ธ๊ฐ„ ๋‡Œ -> ์›์ˆญ์ด ๋‡Œ๋กœ ๋ณ€ํ•˜๋Š” ๊ณผ์ •์„ ์ƒ์ƒํ•˜๊ฒŒ ์ฆ์–ธํ•  ์ˆ˜ ์žˆ๋Š” ์ƒํ™ฉ์ด ๋œ๋‹ค.

    • ์•„๋ฌด๋ฆฌ ๋›ฐ์–ด๋‚œ ์ „๋žต์ด ์žˆ์–ด๋„ ์‹คํ–‰ํ•˜์ง€ ์•Š์œผ๋ฉด ์†Œ์šฉ์ด ์—†๋‹ค.

      • 50์  ์งœ๋ฆฌ ์ „๋žต์„ ๋”ฐ๋ฅด๋Š” ๊ฒƒ์ด 90์  ์งœ๋ฆฌ ์ „๋žต์„ ์ฃผ๊ด€์ ์œผ๋กœ ๊ฐœ์ž…ํ•˜๋Š” ๊ฒƒ๋ณด๋‹ค ๋ฐฑ๋ฐฐ ๋‚ซ๋‹ค

  • ์ž์กด์‹ฌ์„ ๋ฒ„๋ ค๋ผ

    • ํ™•์ฆ ํŽธํ–ฅ, ๊ณผ์ž‰ ํ™•์‹ ์„ ๋ฒ„๋ ค๋ผ

  • ์†์‹ค์€ ๋‹น์—ฐํ•˜๋‹ค

    • ์œ„๋Œ€ํ•œ ํˆฌ์ž์ž๋“ค๋„ ์Šน๋ฅ ์ด 52.4%๋‹ค

  • MDD๋Š” ๋ฌด์กฐ๊ฑด 20% ์•„๋ž˜๋กœ ์œ ์ง€

    • ์ด ๋ฐฉ๋ฒ•์€ ์ดํ›„์— ๋‹ค๋ฃธ

    • MDD๋Š” Max Draw Down์˜ ์•ฝ์–ด๋กœ ์ตœ๋Œ€ ์†์‹ค๋ฅ ์ด๋ผ๊ณ  ์ƒ๊ฐํ•˜๋ฉด ๋œ๋‹ค.

      • 100๋งŒ์›์„ ๊ฐ€์ง€๊ณ  ์žˆ๋Š” ์‚ฌ๋žŒ์ด ํˆฌ์ž๋กœ ์ธํ•ด 25%์˜ MDD๋ฅผ ์–ป์—ˆ๋‹ค๋ฉด ์ž”์—ฌ๊ธˆ์€ 75๋งŒ์›

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